In this paper, we train the DRL model based on the policy gradient
strategy by Algorithm 4. In the initial stage of training, the network
parameters are random, and the output strategy is poor, resulting
in a large number of job waiting. To avoid wasting training time with
useless exploration in the early stage, we sample the episode length
\(\textit {l}\)  from an exponential distribution with a small initial
mean \(\textit {l}_{\text{mean}}\)  [1]}. Considering
that the optimization goal is to minimize the average/total weighted
JCT, the output score \(q_{v}^{i}\)  of node \(v\)  is multiplied by the
weight \(w_{i}\)  of the job \(i\)  to generate a priority score \(p_{v}(1\le v\le |V|)\) .
Finally, a priority list \(P=\)  \(\left(p_{1},p_{2},\ldots ,p_{|V|}\right)\) 
is generated as the scheduling policy, where \(p_{v}(1\le v\le |V|)\) 
corresponds to the priority value of schedulable coflows in the execution
of the job DAG, and a higher \(p_{v}\)  stands for a higher priority.
A formal description of the determination of the priority order is
given in Algorithm 2.

